Download Udemy - Beginning With Machine Learning & Data Science In Python [TP] Torrent

Torrent Contents Size: 542.82 MB

Udemy - Beginning With Machine Learning & Data Science In Python [TP]

1. Working with Machine Learning

1. Exploring Machine Learning and its Types.mp4	MP4	7.33 MB
1. Exploring Machine Learning and its Types.vtt	VTT	5.83 KB
2. Machine Learning Foundations.html	HTML	166 B
3. Install Anaconda.mp4	MP4	8.79 MB
3. Install Anaconda.vtt	VTT	5.56 KB
4. Python Versions.html	HTML	166 B
5. Python and Jupyter Demo.mp4	MP4	17.69 MB
5. Python and Jupyter Demo.vtt	VTT	9.23 KB
5.1 A quick tour of IPython Notebook.zip.zip	ZIP	102.82 KB
6. Python Basics.html	HTML	166 B

2. Understanding Data Wrangling

1. Introduction.mp4	MP4	498.56 KB
1. Introduction.vtt	VTT	281 B
10. Summary.mp4	MP4	539.41 KB
10. Summary.vtt	VTT	396 B
2. Reading from a CSV.mp4	MP4	16.06 MB
2. Reading from a CSV.vtt	VTT	5.86 KB
2.1 Chapter 1 - Reading from a CSV.ipynb.zip.zip	ZIP	395.65 KB
2.2 311-service-requests.zip.zip	ZIP	8.34 MB
3. Selecting data and finding the most common complaint type.mp4	MP4	25.11 MB
3. Selecting data and finding the most common complaint type.vtt	VTT	6.63 KB
3.1 Chapter 2 - Selecting data finding the most common complaint type.ipynb.zip.zip	ZIP	38.79 KB
4. Which borough has the most noise complaints.mp4	MP4	19.48 MB
4. Which borough has the most noise complaints.vtt	VTT	6.23 KB
4.1 Chapter 3 - Which borough has the most noise complaints (or, more selecting data).ipynb.zip.zip	ZIP	18.11 KB
5. Which weekday do people bike the most.mp4	MP4	16.96 MB
5. Which weekday do people bike the most.vtt	VTT	5.75 KB
5.1 bikes.csv.csv	CSV	13.48 KB
5.2 Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb.zip.zip	ZIP	77.77 KB
6. Which month was the snowiest.mp4	MP4	20.43 MB
6. Which month was the snowiest.vtt	VTT	6.57 KB
6.1 Chapter 5 - String Operations- Which month was the snowiest.ipynb.zip.zip	ZIP	78.36 KB
7. Cleaning Messy Data.mp4	MP4	32.02 MB
7. Cleaning Messy Data.vtt	VTT	9.44 KB
7.1 Chapter 6 - Cleaning up messy data.ipynb.zip.zip	ZIP	11.16 KB
8. How to deal with timestamps.mp4	MP4	16.37 MB
8. How to deal with timestamps.vtt	VTT	4.39 KB
8.1 Chapter 7 - How to deal with timestamps.ipynb.zip.zip	ZIP	4.4 KB
8.2 popularity-contest.tsv.tsv	TSV	185.24 KB
9. Loading data from SQL databases.mp4	MP4	13.45 MB
9. Loading data from SQL databases.vtt	VTT	7.41 KB
9.1 Chapter 8 - Loading data from SQL databases.ipynb.zip.zip	ZIP	4.15 KB
9.2 weather_2012_sqlite.zip.zip	ZIP	1.39 KB
9.3 weather_2012.csv.csv	CSV	491.96 KB

3. Linear Regression

1. Introduction.mp4	MP4	1.73 MB
1. Introduction.vtt	VTT	1.18 KB
10. Model evaluation.mp4	MP4	10.73 MB
10. Model evaluation.vtt	VTT	4.8 KB
11. Handling categorical features.mp4	MP4	19.83 MB
11. Handling categorical features.vtt	VTT	8.5 KB
12. Summary.mp4	MP4	5.46 MB
12. Summary.vtt	VTT	2.8 KB
2. What is linear regression.mp4	MP4	2.84 MB
2. What is linear regression.vtt	VTT	1.69 KB
3. The advertising dataset.mp4	MP4	7.09 MB
3. The advertising dataset.vtt	VTT	3.07 KB
3.1 linear regression.zip.zip	ZIP	176.22 KB
4. EDA questions on advertising data.mp4	MP4	4.72 MB
4. EDA questions on advertising data.vtt	VTT	1.77 KB
5. Simple Linear Regression.mp4	MP4	21.9 MB
5. Simple Linear Regression.vtt	VTT	9.83 KB
6. Hypothesis testing and p-values.mp4	MP4	7.84 MB
6. Hypothesis testing and p-values.vtt	VTT	2.91 KB
7. R squared.mp4	MP4	5.78 MB
7. R squared.vtt	VTT	2.62 KB
8. Multiple linear regression.mp4	MP4	15.34 MB
8. Multiple linear regression.vtt	VTT	5.25 KB
9. Model and feature selection.mp4	MP4	7.1 MB
9. Model and feature selection.vtt	VTT	3.34 KB

4. Logistic Regression

1. Introduction.mp4	MP4	891.31 KB
1. Introduction.vtt	VTT	469 B
10. Summary.mp4	MP4	896.84 KB
10. Summary.vtt	VTT	371 B
2. Predicting a continuous response.mp4	MP4	11.59 MB
2. Predicting a continuous response.vtt	VTT	4.12 KB
2.1 logistic regression.zip.zip	ZIP	1.32 MB
3. Quick refresher on linear regression.mp4	MP4	4.91 MB
3. Quick refresher on linear regression.vtt	VTT	1.27 KB
4. Predicting a categorical response.mp4	MP4	15.7 MB
4. Predicting a categorical response.vtt	VTT	5.76 KB
5. Using logistic regression.mp4	MP4	11.37 MB
5. Using logistic regression.vtt	VTT	3.92 KB
6. Probability, odds, log-odds.mp4	MP4	15.05 MB
6. Probability, odds, log-odds.vtt	VTT	5.54 KB
7. What is logistic regression.mp4	MP4	10.91 MB
7. What is logistic regression.vtt	VTT	4.78 KB
8. Interpreting logistic regression.mp4	MP4	16.31 MB
8. Interpreting logistic regression.vtt	VTT	6.27 KB
9. Using logistic regression with categorical features.mp4	MP4	7.25 MB
9. Using logistic regression with categorical features.vtt	VTT	2.66 KB

5. Cross Validation

1. Introduction.mp4	MP4	891.74 KB
1. Introduction.vtt	VTT	457 B
2. Traintest split.mp4	MP4	7.46 MB
2. Traintest split.vtt	VTT	3.64 KB
2.1 cross validation.zip.zip	ZIP	23.81 KB
3. K-fold cross-validation.mp4	MP4	8.04 MB
3. K-fold cross-validation.vtt	VTT	3.67 KB
4. Cross-validation continued.mp4	MP4	15.9 MB
4. Cross-validation continued.vtt	VTT	7 KB
5. Summary.mp4	MP4	4.86 MB
5. Summary.vtt	VTT	2.05 KB

6. Regularization

1. Introduction.mp4	MP4	1.17 MB
1. Introduction.vtt	VTT	668 B
2. Overfitting.mp4	MP4	4.7 MB
2. Overfitting.vtt	VTT	2.36 KB
2.1 regularization.zip.zip	ZIP	366.66 KB
3. Overfitting with linear models.mp4	MP4	12.52 MB
3. Overfitting with linear models.vtt	VTT	6.06 KB
4. Regularizing linear models.mp4	MP4	16.89 MB
4. Regularizing linear models.vtt	VTT	6.96 KB
5. Ridge and Lasso Regularization.mp4	MP4	8.85 MB
5. Ridge and Lasso Regularization.vtt	VTT	3.43 KB
6. Regularization using scikit-learn.mp4	MP4	22.89 MB
6. Regularization using scikit-learn.vtt	VTT	5.63 KB
7. Regularizing logistic models.mp4	MP4	11.19 MB
7. Regularizing logistic models.vtt	VTT	2.08 KB
8. Pipeline and GridSearchCV.mp4	MP4	12.59 MB
8. Pipeline and GridSearchCV.vtt	VTT	3.94 KB
9. Comparing regularized with unregularized models.mp4	MP4	3.22 MB
9. Comparing regularized with unregularized models.vtt	VTT	1.82 KB

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Udemy - Beginning With Machine Learning & Data Science In Python [TP]

Torrent Contents Size: 542.82 MB

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